How big data analytics can help track money laundering

For the past decade, governments around the world have established international anti-money laundering (AML) and counter-terrorist financing efforts in an effort to shut down the cross-border flow of funds to criminal and terrorist organizations. Their success has encouraged criminals to move their cash smuggling away from the financial system to the byzantine world of global trade. According to PwC US, big data analytics are becoming essential to tracking these activities.

“Today’s trade-based money laundering activity goes beyond traditional laundering of criminal funds to include terrorist financing and intentional efforts to circumvent international sanctions,” says Vikas Agarwal, a managing director in PwC’s Advanced Risk & Compliance Analytics practice, which recently released the whitepaper, Goods gone bad: Addressing money-laundering risk in the trade finance system. “To evade detection, traffickers are becoming more sophisticated in their methods, and financial institutions should remain two steps ahead by deploying advanced analytical and statistical techniques. It’s an issue that has become a top concern in board rooms and C-suites across the globe.”

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